Does the amount of time it takes for a page to load in a browser influence search engine rankings for pages? Should it?
If it did, might sites that were all text show up higher in search results than sites that included pictures and different applications? Or, might a search engine find a way to account for different types of sites, and the amount of time it might take them to render in a browser, based upon actual user data in addition to the amount of time it takes a site to render in a browser?
A recent patent application from Yahoo explores ways that a search engine might consider the amount of time it takes different types of pages to render and other issues involving how quickly pages respond to a visits in ranking, classifying and crawling those pages.
Continue reading Does Page Load Time influence SEO?
Meta descriptions for web pages likely don’t influence the rankings of your web pages in search results. But if your meta descriptions include keywords that your pages might be found for, they may be displayed in search results with links to those pages. If those meta descriptions are interesting and engaging, and provide the right information, they may influence people who view them and are interested in what you offer to visit your pages.
When someone searches at a search engine, they are usually presented with a list of search results, often referred to as SERPS – search engine result pages, that can include page titles, abstracts or snippets from those pages, and URLs from the pages. The abstracts or snippets may sometimes be part or all of the meta descriptions for the pages, if the meta descriptions contain the keyword or keywords used by searchers in their queries.
But, a seach engine might just as easily take that snippet from somewhere else on the page returned in search results.
Continue reading Search Engines Evaluating Snippets in SERPS
My favorite travel site doesn’t have a database filled with thousands of hotels or cruises or flights. My favorite travel site doesn’t use words like “amenities,” and it doesn’t change prices on me depending upon the time of day, day of week, week of month, or month of year.
There’s no fancy content management system, live support chat, keyword stuffing of page titles or headings or content, and the word “cheap” doesn’t appear in that title the way that it does in most of the pages that you’ll see if you search for “travel” in one of the major search engines.
The word “sale” doesn’t show up once on my favorite travel site, and I’m not bombarded with information about how much of a percentage I’ll save on my journeys. There’s no inexplicable lawn gnome, or standard stock image of an operator with a headset, or Canadian celebrity, or “top deals” or “packages” on its pages.
If you visit my favorite travel site, you may find yourself imagining that you can smell the salt air wafting through your windows. You may find yourself hearing people enjoying shops and cafes and life, echoing through roads empty of cars, filled with laughter and joy much like they were centuries ago. You may not feel like a tourist at all.
Continue reading My Favorite Travel Site
It’s interesting to see how a search engine might try to ensure the relevancy of its own search results.
A recently granted Yahoo patent investigates an approach that might help it identify how relevant the results it displays to searchers might actually be, and how likely those results are to show a variety of results when a searcher uses a query term that might cover a range of topics.
Before presenting their automated approach for checking relevance and variety, the patent tells us about some of the limitations it sees in using manual review or click data for determining how relevant results might be.
One option for checking on the relevancy of search results would be to manually screen results for each query. That might be pretty time consuming, involve the possibility of human error, and doesn’t seem like it would even begin to cover all of the queries that are conducted on the web.
Continue reading How a Search Engine Might Determine the Relevance of Search Results from Related Queries
Choosing the right words to search with can sometimes be difficult, especially when you search for information about a topic that you don’t know much about. This can be true regardless of whether you might be searching for information on the gravitational effects of binary stars on each other, or the best way to groom a certain breed of dog, or different approaches for making your own homemade icecream.
When you enter some words for your query in a search box, and hit enter, you’ll often see some suggested or related searches in addition to links and descriptions of pages that may be relevant for your search. These suggestions might be at the top of the search results, at the bottom, to one side or another, in the middle of results, or in any or all of those locations.
For example, when I search for [salt water fishing] at the major search engines, I see a number of related terms and other suggestions for my searches.
Continue reading How Search Engines May Decide Upon and Optimize Query Suggestions
Have you ever done a search on Google Local Search like “pizza near empire state building” where you enter a building or a landmark instead of a zip code, or a street, or city or state name?
Pizza Near Empire State Building
While many businesses, organizations, and points of interest (such as parks and schools) have very specific address information associated with them in Google’s Local Search database, people do sometimes want to use landmarks and other more ambiguous locations in their searches, such as neighborhoods (like “pizza in soho”).
A recent patent application from Google pinpoints some of the difficulties that Google’s Local Search may have with searches such as “restaurants near space needle,” where searchers may not be providing much actual geographic information in their searches. It also describes how the search engine might fill in the information it has about locations in its geographic database with user submitted data.
Continue reading Google Geocoding, Ambiguous Locations, and My Maps Submitted Data
Towards the end of 2003, researchers at Microsoft published a paper on a way to analyze the structure and content of Web pages which they called VIPS, or Vision-based Page Segmentation Algorithm. The approach looked at visual and structural aspects of web pages, and meant that a search engine could identify different parts of pages, and possibly understand that some parts could be more important and meaningful than others.
This could possibly have a number of implications for search and information retrieval, and for search engine optimization as well.
Continue reading Breaking Pages Apart: What Automatic Segmentation of Webpages Might Mean to Design and SEO
Search for [ships] in Yahoo Image Search, and you’ll see a good number of images of ships. How do they get placed in the order that they appear within?
A search engine tends to rely upon text associated with those images to rank them in image search. That could be alt text associated with the image, or a caption, or other text that shows up on a page near the picture. Some other information may also be used to rank those images, such as how relevant that page an image appears upon might be for the query term searched for and the quantity and quality of links pointing to the page.
Another signal that a search engine might consider may be the number of times a searcher may select an image when they see that image in search results. A potential problem with using selections or clickthroughs of images as a signal is that many searchers often expect that images (or web pages or news results or videos) near the top of search results tend to be the most relevant for search results and may be more likely to click through images or other kinds of search results at the top of listings that search engines show them.
Imagine a search engine coming up with a prediction model for different queries, where the search engine might predict how often searchers might click on a result at different positions in search results. For example, the top result might be clicked upon 12 percent of the time, and the second result 9 percent of the time, and the third result 6 percent of the time, and so on.
Continue reading Ordering Images (and other Multimedia) in Search Results By Predicting Clickthroughs